Factores culturales que influyen en la adopción de las TIC e internet: una revisión de la literatura
Bibliographic record
Abstract
This study aims to identify the cultural factors that influence the adoption of information and communication technologies (ICT) and the Internet, according to the academic literature, in order to generate a framework for future lines of research that will contribute to the reduction of the digital divide. For this research, a systematized review of research published in Spanish and English in the Scopus database and in the Google Scholar search engine, between 1970 and 2020 in all types of geographic regions, was carried out. As a result of the search, 138 publications were identified, of which 21 were selected and evaluated. The analysis of the information was organized in two stages: in the first, bibliometric data were reviewed and in the second, cultural factors influencing the adoption of ICTs and the Internet. Among the findings was that 81% of the publications were made in urban areas, while South Africa was the country with the highest number of publications. The cultural factors that influence the adoption of ICTs and the Internet are: avoidance of uncertainty, power distance, individualism and masculinity. It should be noted that 43% of the documents found were published more than ten years ago and, due to technological evolution and cultural change in \nthe regions of study: Australia, Fiji, Greece, Iran, Jordan, Malaysia, México, Saudi Arabia, South Korea, South Pacific, United States, countries of the American, Asian, African and European continents, European countries, Pakistan, Canada, South Africa and Taiwan, there is an opportunity to generate new research to explore the current cultural dimensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".